docstr: ChannelTAC Forward.
  
  Args:
      x (torch.Tensor): input feature (batch, C, N, freq, time)
      ref_channel (None or int): index of the reference channel.
  Returns:
      output (torch.Tensor): output feature (batch, C, N, freq, time)
docstr_tok: ["ChannelTAC","Forward",".","Args",":","x","(","torch.Tensor",")",":","input","feature","(","batch",",","C",",","N",",","freq",",","time",")","ref_channel","(","None","or","int",")",":","index","of","the","reference","channel",".","Returns",":","output","(","torch.Tensor",")",":","output","feature","(","batch",",","C",",","N",",","freq",",","time",")"]
code: def forward(self, x, ref_channel=None):
      """ChannelTAC Forward.
  
      Args:
          x (torch.Tensor): input feature (batch, C, N, freq, time)
          ref_channel (None or int): index of the reference channel.
      Returns:
          output (torch.Tensor): output feature (batch, C, N, freq, time)
      """
      batch = x.contiguous().permute(0, 4, 1, 3, 2)  # [B, T, C, F, N]
      out = self.transform(batch)
      out_mean = self.average(out.mean(dim=2, keepdim=True)).expand_as(
          out
      )
      out = self.concat(torch.cat([out, out_mean], dim=-1))
      out = out.permute(0, 2, 4, 3, 1) + x
      return out
  
imports: import warnings
  import torch
  import torch.nn
  from espnet2.enh.layers.dptnet import ImprovedTransformerLayer
  from espnet2.enh.layers.tcn import ChannelwiseLayerNorm
  from espnet2.torch_utils.get_layer_from_string import get_layer
label_desc: Use image node_id 2 for calling the ChannelTAC obj's underlying member method code with example usage: obj.forward(x, ref_channel) and returns: out